1. Technical Field
In recent years, image processing technologies have been greatly developed, and image-processing apparatus for remote-sensing image processing, color image processing and file image optical character recognition (OCR) etc. have gained wide applications.
2. Background Art
In these image processing technologies, gray-level image binarization process is an indispensable. A binarization process is a process for converting a color image or a gray-level image into a black-white image, wherein such a black-white image has only two gray-levels.
Conventionally-used binarizing methods are well-known, such as a binarizing method by simply providing a preset threshold for the entire image to be binarized, a binarizing method using Otsu maximum inter-category square difference method for calculating a threshold based on a gray-level histogram (see “A Threshold Selection Method from Gray-Level Histograms”, IEEE Trans. On systems, Man, and cybernetics, Vol. SMC-9, No. 1, pp. 62–66, January 1979); Kittler and Illingworth's minimum error method (see “Minimum Error Thresholding” Pattern Recognition. Vol. 19, No. 1, pp. 41–47, 1986); and Tsai's moment preserving method (see “Moment-Preserving thresholding: A New Approach”, Computer Vision, Graphics, and Image Processing 29, pp. 377–393, 1985), etc.
However, binarizing methods using a single threshold for an entire image often have their defects. For example, at the time of performing OCR process on a file image, and at the time of performing binarizing process on a file image of characters, tables, and pictures having various gray-levels, it is very difficult to completely preserve the characters, tables, and pictures using a single threshold.
So methods have been used that divide an image into a plurality of non-overlapping sub-images, calculate a gray-level histogram for each of the sub-images so as to determine a corresponding threshold for each of the sub-images, and then perform binarization process. For example, Japanese Patent Laid-open 9-233326 describes a binarizing method, which divides an image into sub-images each having 64×64 pixels, calculates a threshold of one of the sub-images using the gray-level histogram of the sub-image, and performing a smoothing process for each pixels in the sub-image using interpolation, and finally performing binarization process for each of the pixels.
In table image recognition systems, such as note processing systems used in banks, it is necessary to perform binarization process on the input gray-level images to convert them into binarized images (black-white images), perform a match-discrimination process for table images based on the outer frames, sizes, and relative positions of the tables, determine the sequence of the tables as registered in the system, determine the areas to be recognized, and carry OCR process over the areas to be recognized. Obviously, binarization process is a very important process, and if the frame of a table is lost or is not clear, the match-discrimination process may be led to failure; if noise in the areas to be recognized is too great, the accuracy of OCR will be seriously affected. As the outer frames in images of tables are different in their thickness and background patterns or patterns left by carbon paper often exist in the area to be recognized, conventional binarization methods often show poor results when they are used in processing images of tables.